R Programming Introduction

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this class, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The also shows you real data analysis in action by covering everything from importing data to publishing your results.

Audience

This class is for the non-programmer who wants to gain a basic understanding of R.

Prerequisites

No prior programming experience is necessary. Some background in statistics is helpful, but not necessary.

Course Objectives

• Write a simple R program, and discover what the language can do
• Use data types such as vectors, arrays, lists, data frames, and strings
• Execute code conditionally or repeatedly with branches and loops
• learn R add-on packages

Course Outline

The R Language

1. Introduction

• What Is R?
• Installing R
• Choosing an IDE
• Emacs + ESS
• Eclipse/Architect
• RStudio
• Revolution-R
• Live-R
• Other IDEs and Editors
• Your First Program
• How to Get Help in R
• Installing Extra Related Software

2. A Scientific Calculator

• Mathematical Operations and Vectors
• Assigning Variables
• Special Numbers
• Logical Vectors
• Summary
• Test Your Knowledge: Quiz
• Test Your Knowledge: Exercises

3. Inspecting Variables and Your Workspace

• Classes
• Different Types of Numbers
• Other Common Classes
• Checking and Changing Classes
• Examining Variables
• The Workspace

4. Vectors, Matrices, and Arrays

• Vectors
• Sequences
• Lengths
• Names
• Indexing Vectors
• Vector Recycling and Repetition
• Matrices and Arrays
• Creating Arrays and Matrices
• Rows, Columns, and Dimensions
• Row, Column, and Dimension Names
• Indexing Arrays
• Combining Matrices
• Array Arithmetic

5. Lists and Data Frames

• Lists
• Creating Lists
• Atomic and Recursive Variables
• List Dimensions and Arithmetic
• Indexing Lists
• Converting Between Vectors and Lists
• Combining Lists
• NULL
• Pairlists
• Data Frames
• Creating Data Frames
• Indexing Data Frames
• Basic Data Frame Manipulation

6. Environments and Functions

• Environments
• Functions
• Creating and Calling Functions
• Passing Functions to and from Other Functions
• Variable Scope

7. Strings and Factors

• Strings
• Constructing and Printing Strings
• Formatting Numbers
• Special Characters
• Changing Case
• Extracting Substrings
• Splitting Strings
• File Paths
• Factors
• Creating Factors
• Changing Factor Levels
• Dropping Factor Levels
• Ordered Factors
• Converting Continuous Variables to Categorical
• Converting Categorical Variables to Continuous
• Generating Factor Levels
• Combining Factors

8. Flow Control and Loops

• Flow Control
• if and else
• Vectorized if
• Multiple Selection
• Loops
• repeat Loops
• while Loops
• for Loops

9. Advanced Looping

• Replication
• Looping Over Lists
• Looping Over Arrays
• Multiple-Input Apply
• Instant Vectorization
• Split-Apply-Combine
• The plyr Package

10. Packages

• Loading Packages
• The Search Path
• Libraries and Installed Packages
• Installing Packages
• Maintaining Packages

11. Dates and Times

• Date and Time Classes
• POSIX Dates and Times
• The Date Class
• Other Date Classes
• Conversion to and from Strings
• Parsing Dates
• Formatting Dates
• Time Zones
• Arithmetic with Dates and Times
• Lubridate